基于卷积神经网络与迁移学习的油茶病害图像识别

Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning

传统的植物病害图像识别准确率严重依赖于耗时费力的人工特征设计。该文利用深度卷积神经网络强大的特征学习和特征表达能力来自动学习油茶病害特征,并借助迁移学习方法将Alex Net模型在Image Net图像数据集上学习得到的知识迁移到油茶病害识别任务。对油茶叶片图像进行阈值分割、旋转对齐、尺度缩放等预处理后,按照病害特征由人工分为藻斑病、软腐病、煤污病、黄化病和健康叶5个类别。每个类别各选取750幅图像组成样本集,从样本集中随机选择80%的样本用作训练集,剩余20%用作测试集。利用随机裁剪、旋转变换和透视变换对训练集进行数据扩充,以模拟图像采集的不同视角和减少网络模型的过拟合。在Tensor Flow深度学习框架下,基于数据扩充前后的样本集,对Alex Net进行全新学习和迁移学习。试验结果表明,迁移学习能够明显提高模型的收敛速度和分类性能;数据扩充有助于增加数据的多样性,避免出现过拟合现象;在迁移学习和数据扩充方式下的分类准确率高达96.53%,对藻斑病、软腐病、煤污病、黄化病、健康叶5类病害的F1得分分别达到94.28%、94.67%、97.31%、98.34%和98.03%。该方法具有较高的识别准确率,对平移、旋转具有较强的鲁棒性,可为植物叶片病害智能诊断提供参考。

Leaf diseases are a serious problem in Camellia oleifera production.The occurrence of Camellia oleifera disease is affected by various factors,such as variety,cultivation environment,climate condition and management level.The key to effective prevention and cure of Camellia oleifera disease is to identify the disease type timely and accurately.Traditional computer vision methods for plant leaf disease recognition depend heavily on time-consuming and elaborate feature design.To solve this problem,a recognition model of Camellia olerfera leaf diseases based on convolutional neural network was proposed and transfer learning was used to improve model’s performance.Deep convolutional neural network has powerful capacities of feature learning and feature expression,which was used to learn features of diseased Camellia oleifera leaves.Transfer learning method was used to transfer the knowledge learned from ImageNet dataset by AlexNet to the identification task of Camellia oleifera diseases.The proposed model was implemented with Python programming language under the deep learning framework of Tensorflow by modifying the output number of the last fully connected layers in AlexNet to 5.We collected Camellia oleifera leaves in artificial Camellia oleifera land and took photos by mobile phone in bright indoor environment after flattening leaves.Leaf images were first converted from RGB(red,green,blue)color space to HSI(hue,saturation,intensity)color space,and then background was removed by threshold segmentation on hue and saturation channels.After segmentation,morphological open and close operations with a radius of 3 pixels were performed to remove burrs,holes and other noises,and thus the leaf mask was obtained by filling holes.Leaf mask was multiplied with the original image to obtain the colored leaf region.The colored leaf region was then rotated according to its principal axis angle and aligned horizontally.Based on the long edge,leaf image was scaled to 256×256 pixels.After these pretreatments,Camellia oleifera leaf images were manually identified as algal spot,soft rot,sooty mould,yellows and healthy leaf.A total of 750 images for each disease category were selected to form data set,80%of samples were randomly selected for train set,and the remaining 20%for test set.To simulate different views of image acquisition and reduce over-fitting of network models,image datasets of diseased Camellia oleifera leaf were augmented by random crop,random rotation and random perspective transformation.To save space for huge amount of augmented images,data augmentation was executed online when training.In random crop mode,image is randomly cropped from 256×256 to 227×227 pixels.In random rotation mode,image is randomly rotated by 0,90,180,or 270 degrees.In order to avoid serious distortion of the transformed image,the displacement of the corresponding point in perspective transformation is limited to 10%of the image width and height.A total of 54 experiments were performed on Nvidia GPU with a combination of 2 learning methods(training from scratch,transfer learning),3 data augmentation modes(no augmentation,random cropping,sequential execution of random cropping,perspective transformation and rotation),3 regularization coefficients(0.0,0.0005,0.0001),and 3 initial learning rates(0.001,0.005,0.01).When training from scratch,weights are randomly initialized with truncated normal distribution and biases are initialized with zero constant.In transfer learning,only the last fully connected layers’weights and biases are reinitialized with random values,and those of other layers are assigned by the values from pre-trained AlexNet model.Experimental results show that transfer learning can significantly improve models’convergence speed and classification performance,and data augmentation can enrich data diversity and avoid over fitting especially when training from scratch.The classification accuracy was as high as 96.53%in transfer learning,and the F1 scores of algal spot,soft rot,sooty mould,yellows and healthy leaf achieved 94.28%,94.67%,97.31%,98.34%and 98.03%respectively.This method has high recognition accuracy,and strong robustness to translation and rotation,and can provide references for intelligent diagnosis of plant leaf diseases.